Alexa, do men talk too much?
Who is this presentation for?
- People in technology and data scientists
Numerous studies have shown that men consistently interrupt women in professional and nonprofessional settings. There’s also a tendency to provide remedial explanations to concepts that are already well known to the target audience. A new word, mansplaining, was added to the Merriam-Webster dictionary that captures this social problem. At its core, mansplaining is when a man talks condescendingly to someone (especially a woman) about something he has incomplete knowledge of with the mistaken assumption that he knows more about it than the person he’s talking to does.
This phenomenon impacts women in all professions at all levels of their career. Any form of privileged explaining can cause women to be uncomfortable in their own careers and can keep women from entering male-dominated fields such as STEM. For women already in these fields, being talked over in a professional setting like a meeting isn’t just frustrating but could be career limiting.
Sireesha Muppala, Shelbee Eigenbrode, and Emily Webber explore this in two ways. First, they build an artificial intelligence- (AI) and machine learning- (ML) based solution that explores the prevalence of mansplaining in professional settings. Then they explore strategies that can be employed to addresses the issue at hand. The goal is to demonstrate the feasibility of using the combination of technology and human actions to raise awareness about and address prevalent issues that have a direct impact on women in technology.
For the technical solution, they used the Kaggle Gender Recognition by Voice dataset. ML, using algorithms that continuously assess and learn from data, enables building automated analytical models that provide insights into hidden patterns in the data. AI-based natural language processing (NLP) and automatic speech recognition (ASR) provides the ability to access these insights in a conversational manner.
The combination of ML and AI were used to build an Alexa skill that can be used to explore questions such as whether or not men talk more than women in meetings, how many women speak up in meetings versus men, and what percentage of women participate in a meeting. The technical solution helps in identifying and raising awareness of the issue, but doesn’t solve the problem of gender identity and equality in professional settings.
Sireesha, Shelbee, and Emily explore strategies for women such as owning the responsibility to have their voices heard by speaking up, asking questions, being an ally to other women, and ensuring young girls and women in technology are getting mentors. They also provide strategies for men, such as being empathetic and realizing that the problem is real and taking feedback from the female colleagues about the issue in order to be more self aware during interactions.
This is crucial now. The number of women in technology is on a downward trend. Just in the field of computer science, in 1984 women held 37% of degrees. Today that number is down to 19%. Even when women do choose STEM careers, only 26% work in technical roles, compared to 40% of men. In technology, women leave the industry at a rate 45% higher than their male peers. Addressing gender identity and gender equality issues at workplaces is a crucial step to reverse the trend.
What you'll learn
- Gain an increased awareness of the mansplaining phenomenon in a way that combines technology and guidance around effective communication to drive change in professional conversations
- Discover an Alexa skill using ML that can objectively identify mansplaining, which, although it generally refers to men talking down to women, can happen within gender
Amazon Web Services
Sireesha Muppala is a solutions architect at Amazon Web Services. Her area of depth is machine learning and artificial intelligence, and she provides guidance to AWS customers on their ML and AI workloads. She led the Colorado University team to win and successfully complete a two-year research grant from the Air Force Research Lab on “Autonomous Job Scheduling in Unmanned Arial Vehicles.” She’s an experienced public speaker and has presented research papers at international conferences, such as CoSAC: Coordinated Session-Based Admission Control for Multi-Tier Internet Applications at the IEEE International Conference on Computer Communications and Networks (ICCCN) and Regression Based Multi-Tier Resource Provisioning for Session Slowdown Guarantees at the IEEE International Conference Performance, Computing and Communications (IPCCC). She’s published technical articles, such as Coordinated Session-Based Admission Control with Statistical Learning for Multi-Tier Internet Applications in the Journal of Network and Computer Applications (JNCA), Regression-Based Resource Provisioning for Session Slowdown Guarantee in Multi-Tier Internet Servers, and Multi-Tier Service Differentiation: Coordinated Resource Provisioning and Admission Control in the Journal of Parallel and Distributed Computing (JPDC). Sireesha earned her PhD and postdoctorate from the University of Colorado, Colorado Springs, while working full time. Her PhD thesis is Multi-Tier Internet Service Management Using Statistical Learning Techniques.
Amazon Web Services
Shelbee Eigenbrode is a solutions architect at Amazon Web Services (AWS). Her current areas of depth include DevOps combined with machine learning and artificial intelligence. She’s been in technology for 22 years, spanning multiple roles and technologies. Previously, she spent 20+ years at IBM. She’s a published author, blogger, and vlogger evangelizing DevOps practices with a passion for driving rapid innovation and optimization at scale. In 2016, she won the DevOps dozen blog of the year demonstrating what DevOps is not. With over 26 patents granted across various technology domains, her passion for continuous innovation combined with a love of all things data recently turned her focus to data science. Combining her backgrounds in data, DevOps, and machine learning, her passion is helping customers embrace data science and ensure all data models have a path to use. She also aims to put ML in the hands of developers and customers who are not classically trained data scientists.
Amazon Web Services
Emily Webber is a machine learning specialist solutions architect at Amazon Web Services (AWS). She guides customers from project ideation to full deployment, focusing on Amazon SageMaker, where her customers are household names across the world, such as T-Mobile. She’s been leading data science projects for many years, piloting the application of machine learning into such diverse areas as social media violence detection, economic policy evaluation, computer vision, reinforcement learning, the IoT, drones, and robotic design. Previously, she was a data scientist at the Federal Reserve Bank of Chicago and a solutions architect for an explainable AI startup in Chicago. Her master’s degree is from the University of Chicago, where she developed new applications of machine learning for public policy research with the Data Science for Social Good Fellowship.
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